73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
import pytest
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np = pytest.importorskip("numpy")
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npt = pytest.importorskip("numpy.testing")
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scipy = pytest.importorskip("scipy")
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import networkx as nx
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from networkx.generators.degree_seq import havel_hakimi_graph
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class TestSpectrum:
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@classmethod
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def setup_class(cls):
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deg = [3, 2, 2, 1, 0]
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cls.G = havel_hakimi_graph(deg)
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cls.P = nx.path_graph(3)
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cls.WG = nx.Graph(
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(u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
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)
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cls.WG.add_node(4)
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cls.DG = nx.DiGraph()
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nx.add_path(cls.DG, [0, 1, 2])
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def test_laplacian_spectrum(self):
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"Laplacian eigenvalues"
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evals = np.array([0, 0, 1, 3, 4])
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e = sorted(nx.laplacian_spectrum(self.G))
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npt.assert_almost_equal(e, evals)
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e = sorted(nx.laplacian_spectrum(self.WG, weight=None))
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npt.assert_almost_equal(e, evals)
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e = sorted(nx.laplacian_spectrum(self.WG))
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npt.assert_almost_equal(e, 0.5 * evals)
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e = sorted(nx.laplacian_spectrum(self.WG, weight="other"))
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npt.assert_almost_equal(e, 0.3 * evals)
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def test_normalized_laplacian_spectrum(self):
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"Normalized Laplacian eigenvalues"
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evals = np.array([0, 0, 0.7712864461218, 1.5, 1.7287135538781])
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e = sorted(nx.normalized_laplacian_spectrum(self.G))
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npt.assert_almost_equal(e, evals)
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e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight=None))
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npt.assert_almost_equal(e, evals)
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e = sorted(nx.normalized_laplacian_spectrum(self.WG))
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npt.assert_almost_equal(e, evals)
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e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight="other"))
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npt.assert_almost_equal(e, evals)
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def test_adjacency_spectrum(self):
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"Adjacency eigenvalues"
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evals = np.array([-np.sqrt(2), 0, np.sqrt(2)])
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e = sorted(nx.adjacency_spectrum(self.P))
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npt.assert_almost_equal(e, evals)
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def test_modularity_spectrum(self):
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"Modularity eigenvalues"
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evals = np.array([-1.5, 0.0, 0.0])
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e = sorted(nx.modularity_spectrum(self.P))
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npt.assert_almost_equal(e, evals)
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# Directed modularity eigenvalues
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evals = np.array([-0.5, 0.0, 0.0])
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e = sorted(nx.modularity_spectrum(self.DG))
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npt.assert_almost_equal(e, evals)
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def test_bethe_hessian_spectrum(self):
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"Bethe Hessian eigenvalues"
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evals = np.array([0.5 * (9 - np.sqrt(33)), 4, 0.5 * (9 + np.sqrt(33))])
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e = sorted(nx.bethe_hessian_spectrum(self.P, r=2))
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npt.assert_almost_equal(e, evals)
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# Collapses back to Laplacian:
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e1 = sorted(nx.bethe_hessian_spectrum(self.P, r=1))
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e2 = sorted(nx.laplacian_spectrum(self.P))
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npt.assert_almost_equal(e1, e2)
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